1. Scatterplot AKA bivariate plot
2. Line-graph
3. Histogram
4. Boxplot
5. Barplot AKA Barchart AKA bargraph

What's not great about this plot, especially near (0, 0)?

This is called overplotting: when points are stacked so densely we can't see what's going on!

There are two ways of dealing with this:

1. Make points a little more transparent
2. Jiggle the points a little

A statistical graphic is a mapping of data variables to aes()thetic attributes of geom_etric objects.

The five named graphs we'll see in this class. Note: I reordered them from last time to be easiest to hardest to work with:

1. Scatterplot AKA bivariate plot
2. Line-graph
3. Histogram
4. Boxplot
5. Barplot AKA Barchart AKA bargraph

• We are building up to doing data visualization in R via the ggplot2 package
• Last time we reverse-engineered the grammar from graphical outputs
• Today we (forward) engineer them

In tidy format:

• Today we kick off Topic 2.b) Data Visualization by asking ourselves: What is a statistical graphic?
• But a brief lesson from military history first

In 1812, Napoleon led a French invasion of Russia, marching on Moscow.

It was one of the biggest military disasters ever, in particular b/c of the Russian winter.

Famous graphical illustration of Napolean's march to/from Moscow

This was considered a revolution in statistical graphics because between

• the map on top
• the line graph on the bottom

there are 6 dimensions of information (i.e. variables) being displayed on a 2D page.

A statistical graphic is a mapping of data variables to aes()thetic attributes of geom_etric objects.

From ggplot2movies package, the movies data set:

From nycflights13 package, the flights data set:

From okcupiddata package, the profiles data set:

From fueleconomy package, the vehicles data set:

From babynames package, the babynames data set:

Say hello to the 5NG: the five named graphs

1. Scatterplot AKA bivariate plot
2. Line-graph
3. Histogram
4. Boxplot
5. Barplot AKA Barchart AKA bargraph

• There are many ways to organize data. Today we learn one way: the "tidy data" format.
• It is rather simple, but deceptively powerful.
• Equivalent to "long format"

1. Each observation forms a row
2. Each variable forms a column
3. Each type of observational unit forms a table

1. Each observation forms a row: In other words, each row corresponds to a single observational unit
2. Each variable forms a column:
   • Some of the variables may be used to identify the observational units. For organizational purposes, it's generally better to put these in the left-hand columns
   • Some of the variables may be observed values associated with each observational unit
3. Each type of observational unit forms a table: Don't mix apples and oranges, keep apples with apples and oranges with oranges

The nycflights13 package contains "tidy data" all 336,776 flights that departed from NYC (e.g. EWR, JFK and LGA) in 2013.

To help understand what causes delays, it also includes a number of other useful datasets.

• weather: hourly meterological data for each airport
• planes: construction information about each plane
• airports: airport names and locations
• airlines: translation between two letter carrier codes and names

In small teams, take 3 minutes to write down

1. A couple of male and female names that are "modern"
2. A couple of male and female names that are "old-fashioned"
3. One male and one female name that are "back in vogue"

• Computers are stupid! You need to:
  • Tell it exactly and everything it needs to do
  • Everything needs to be perfect:
    • Write everything from scratch
    • Names of "stuff" need to typed exactly
    • Parentheses need to match
• Recall: This is not a class on programming/coding. However, we'll learn just enough to do statistics and data science
• Side Benefit: Many of the concepts translate to almost all programming languages: python, javascript, etc.

Recall the tradeoff:

Less of this…	More of this…

• Base R, i.e. R straight out of the box. It's fairly limited in power and functionality.
• R Packages are extensions to R that are
  • contributed by a world-wide community of R users
  • extend base R's functionality
  • are downloadable over the internet from RStudio.

You need to install each package once.

• In RStudio: Go to Files Panel -> Packages -> Install
• Type in the package name and click install
• The procedure for updating a package is the same

You need to load a package everytime you want to use it.

• Run library(PACKAGENAME) in the console.

Today's Learning Check: Install and then load 3 packages:

• dplyr: a package for data manipulation
• ggplot2: a package for data visualization
• babynames: a package of baby name data

The babynames package contains for each year from 1880 to 2013, the number of children born of each sex given each name in the United States. Only names with more than 5 occurrences are considered.

• In catalog: Introduction to Statistical Sciences
• New: Introduction to Statistical and Data Sciences

• Example domains: biology, economics, physics, sociology, etc.
• So why the title switch?

Have students engage in the data/science research pipeline in as faithful a manner as possible while maintaining a level suitable for novices.

• Cobb: Minimizing prerequisites to research
• Not necessarily publishing in top journals, but answering scientific questions with data.
• Difficult to do research without understanding stats, however

We will, as best we can, perform all this:

And not just this, as in many previous intro stats courses:

Foster a conceptual understanding of statistical topics and methods using simulation/resampling and real data whenever possible, rather than mathematical formulae.

• Whenever we can, use real data
• Example data set: nycflights13
• There are two "engines" that can make statistics "work"
  • Mathematics: formulas, approximations, etc
  • Computers: simulations, random number generation

In this course, computers and not math will be the "engine". What does this mean?

• Less of this:
  
• But more of this:
  

• Previous programming/coding experience is not a prerequisite to this course
• This course is not an explicit course on programming, coding, nor computer science. But we will use some elements.
• Also you will be exposed to basic algorithmic thinking and computational logic
• Learning R is like learning a foreign language: its really hard at first!

• Computers are stupid!
• When learning, take existing code that works, and tweak it!

Blur the traditional lecture/lab dichotomy of introductory statistics courses by incorporating more computational and algorithmic thinking into the syllabus.

• Completely separate lecture and labs is a legacy of a time before
  

• Not all laptops are created equal: operating system, processing power, age
• RStudio Server: cloud-based version of RStudio where all processing is done on Middlebury servers
• go/rstudio/ (on campus or via VPN)

Develop statistical literacy by, among other ways, tying in the curriculum to current events, demonstrating the importance statistics plays in society.

• H.G. Wells (paraphrased): "Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write."
• Me: "Sure, it's easy to lie with statistics. But it's also hard to tell the truth without them."

• Capstone experience to align this topics and principles of this course with how research and learning is done in practice.
• Work on interpersonal and collaborative skills. No textbook on that!

Either

• Lab format: With laptop
  • You sit in groups of 4
  • I'll talk for 10-15 minutes before you work on learning checks
• Chalk talk: Old-school
  • Keep desk in rows
  • More traditional lecture format

• R: Software behind the scenes i.e. the engine
• RStudio: Intergrated development environment i.e. the interface
• DataCamp: Browser-based learning tool i.e. the driver's ed teacher

• Login to go/rstudio/ with your Midd account
• If you don't have access, raise your hand. (Username: guest1, password: rstudioguest)
• In RStudio menu bar -> File -> New File -> R Script

1. Console: Crunch numbers in R
2. Files, Packages, Help: See your files, install packages, help files
3. Editor: Where you'll write code and save it
4. Environment: Your workspace

• This is where you run/execute commands
• The ">" is the prompt. It means R is ready to receive commands
• If you don’t see a ">" and want to restart, press ESC.

Now we will use R via DataCamp instead of via RStudio, but just for driver's ed. Two panels exist in both:

1. Editor panel: Where you write code
2. Console panel: Where you will execute code